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LinQuer: Linkage Query Writer |
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Try a preview of
LinQuer's web interface here. |
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Project Description |
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Discovering
links between different entities in data sources is a challenging task
and an attractive research area. Existence of links add value to data
sources, enhance data access and information discovery, and allow or
enhance many increasingly important data mining tasks. When data
sources are not linked, they
resemble islands
of data (or data
silos), where each
island maintains only part of the data necessary to satisfy a user's
information needs. Penetrating these silos to both understand their
contents and
understand potential semantic connections is a daunting task. What
users and data publishers need is automated support for creating
referential links between data that reside in different sources and
that are semantically related. Finding such links often
requires the use of approximate matching (to overcome syntactic
representational differences and errors) and semantic matching (to find
specific semantic relationships). Furthermore, both types of
matching must be tightly integrated to accommodate for the tremendous
heterogeneity found in the data that reside in today's information
systems.
LinQuer is a system for generating SQL queries for semantic link
discovery over relational data. The LinQuer framework consists of
* LinQL, a language for specification of
linkage requirement
* A web interface and an API for
translating LinQL queries to standard SQL queries
* An interface that assists users in
writing LinQL queries.
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People |
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Publications |
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- Framework
for Semantic Link Discovery Over Relational Data
O. Hassanzadeh, A. Kementsietsidis, L. Lim, R. J.
Miller and M. Wang.
Proceedings of the 18th ACM Conference on Information and Knowledge
Management (CIKM 2009)
- Linkage
Query Writer
O. Hassanzadeh, R. Xin, R. J. Miller, A. Kementsietsidis, L. Lim, and
M. Wang.
Proceedings of the VLDB Endowment VLDB Endowment, Volume 2 Issue 2,
August 2009 (Demonstration Track)
- LinkedCT:
A Linked Data Space for
Clinical Trials
O. Hassanzadeh, A. Kementsietsidis, L. Lim, R. J. Miller, and
M. Wang.
Technical Report 569 - University of Toronto
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Datasets and Experimental Results |
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